Imagga at Hack The Visual
During the last couple years we’ve been taking part in numerous hackathons and events. Hack The Visual was perfect fit for what we do at Imagga. The goal of the event is to connect different types of visual data to each other and create new and interesting prospective. All data is welcomed - pictures, music, video, geo-data, even open data, you name it. In just 48 hours over 100 participants were hacking on projects mashing existing APIs and data sets to find a solution for a real life visual problem.
The main challenge was to bring together photos, videos and other kind of imagery with hardware, interfaces, platforms, apps & services in order to unlock the next step in visual culture.
Tree main tracks have been set based on research by Imaging Mind (organizer of the event) regarding the future of imaging:
- meshed capture - connecting multiple camera sources to generate new experiences. Winner: Camera Crowd - combining multiple photos and their location data with a photo of the area you are. A mesh of pictures from different sources blended into the space
- new perspectives/interpretation of images - accessing various image data sets to extract value from them outside of the image itself. Winner: Hear The Picture - by linking each coloured pixel to its individual sound, a photo could be ‘heard’ through its own distinctive soundtrack
- interactive visuals - reworking the static images into interactive new experience. Winner: Sharon - watch the same video source with multiple people, and allow synced manipulation of the video
Grand Prize went to Splatmap - web application that allows you to photograph buildings with your smartphone and plot the information into the application.
The special Imagga API prize went to Remember - app that triggers your memories using your own photo collection. Re/Visit a place and Remember will remind you of pictures you or someone else snapped nearby. It can also search for relevant photos based on the topics in the photo (using Imagga’s image recognition tagging API), turning your photo library into a smart conversation starter wherever you might be.
Overall, great event! See you next time. And do not forget to give Imagga APIs a try!
Imagga and 6 alternative image recognition services
In a recent post on the newly introduced component of Wolfram’s language for image identification ImageIdentify Jordan Novet of Venture Beat conducted a quick test of ImageIdentify against 5 deep learning platforms for image recognition he chose. He selected “10 images from Flickr that seemed to clearly fall into the 1,000 categories used for the 2014 ImageNet visual recognition competition.” and tagged them with ImageIdentify and 5 alternative image recognition services.
As one of the very first platforms as a service, offering such functionality worldwide, we felt we should join this funny experiment and make our humble contribution, by adding the tags Imagga’s image recognition technology generated for the same 10 photos. You can try with your own photos using Imagga online image recognition demo.
We better leave the results speak for themselves. Please take all this with a grain of salt and don’t forget that these results are obtained for just 10 randomly selected photos :)
1. Coffee Mug
Imagga: cup, mug, coffee mug, drinking vessel, beverage, punch, container, coffee, drink, vessel
Wolfram ImageIdentify: tea
CamFind: white ceramic mug
Clarifai: coffee cup nobody tea mug cafe hot ceramic coffee cup cutout
MetaMind: Coffee mug
Orbeus: cup
AlchemyAPI: coffee
2. Mushroom
Imagga: vegetable, produce, mushroom, food, fungus, cap, organic, lush, moss, forest
Wolfram ImageIdentify: magic mushroom
CamFind: white mushroom
Clarifai: mushroom fungi fungus toadstool nature grass fall moss forest autumn
MetaMind: Mushroom
Orbeus: fungus
AlchemyAPI: mushroom
3. Spatula
Imagga: microphone, spatula, business, turner, black, device, knife, technology, hand, cooking utensil
Wolfram ImageIdentify: spatula
CamFind: black kitchen turner
Clarifai: steel wood knife handle iron fork equipment nobody tool chrome
MetaMind: spatula
Orbeus: tool
AlchemyAPI: knife
4. Scoreboard
Imagga: signboard, scoreboard, board
Wolfram ImageIdentify: scoreboard
CamFind: baseball scoreboard
Clarifai: scoreboard soccer stadium football game competition goal group north america match
MetaMind: Scoreboard
Orbeus: billboard
AlchemyAPI: sport
5. German Shepherd
Imagga: shepherd dog, german shepherd, dog, canine, domestic animal, kelpie, doberman, pinscher, pet, animal
Wolfram ImageIdentify: German shepherd
CamFind: black and brown German shepherd
Clarifai: dog canine cute puppy mammal loyalty grass sheepdog fur German hepherd
MetaMind: German Shepherd, German Shepherd Dog, German Police Dog, Alsatian
Orbeus: animal
AlchemyAPI: dog
6. Toucan
Imagga: volleyball, ball, people, man, black, racket, body, person, game equipment, equipment (nice try)
Wolfram ImageIdentify: tufted puffin
CamFind: toucan bird
Clarifai: bird one north america nobody animal people adult nature two outdoors
MetaMind: toucan
Orbeus: animal
AlchemyAPI: sport
7. Indian Cobra
Imagga: Indian cobra, cobra, snake, thunder snake
Wolfram ImageIdentify: black-necked cobra
CamFind: brown and beige cobra snake
Clarifai: snake nobody reptile cobra wildlife daytime sand rattlesnake north america desert
MetaMind: Indian cobra, Naja Naja
Orbeus: animal
AlchemyAPI: snake
8. Strawberry
Imagga: berry, strawberry, fruit, edible fruit, produce, food, strawberries, juicy, sweet, dessert
Wolfram ImageIdentify: strawberry
CamFind: red strawberry ruit
Clarifai: fruit sweet food strawberry ripe juicy berry healthy isolated delicious
MetaMind: strawberry
Orbeus: strawberry
AlchemyAPI: berry
9. Wok
Imagga: plate, pan, wok, china, porcelain, food, dinner, cooking utensil, utensil, delicious
Wolfram ImageIdentify: cooking pan
CamFind: gray steel frying pan
Clarifai: ball nobody pan cutout kitchenware north america tableware competition bowl glass
MetaMind: wok
Orbeus: frying pan
AlchemyAPI: (No tags)
10. Shoe store
Imagga: black, symbol, business, food, design, pattern, sign, art, traditional
Wolfram ImageIdentify: store
CamFind: black crocs
Clarifai: colour street people color car mall road fair architecture hotel
MetaMind: Shoe Shop, Shoe Store
Orbeus: shoe shop
AlchemyAPI: sport
The fun part aside, we are quite interested to see soon a more comprehensive subjective and objective evaluation of all these services, including Imagga, with their pros and cons, on more representative and rich datasets, and depending on the way the tags will be used in different verticals and applications.
Competition is an important driver for every industry, so we are more than open to participate in such kinds of service comparisons and may even initiate such a comparison in the very near future.
Batch Image Processing From Local Folder Using Imagga API
This blog post is part of series on How-Tos for those of you who are not quite experienced and need a bit of help to set up and use properly our powerful image recognition APIs.
In this one we will help you to batch process (using our Tagging or Color extraction API) a whole folder of photos, that reside on your local computer. To make that possible we’ve written a short script in the programming language Python: https://bitbucket.org/snippets/imaggateam/LL6dd
Feel free to reuse or modify it. Here’s a short explanation what it does. The script requires the Python package, which you can install using this guide.
It uses requests’ HTTPBasicAuth to initialize a Basic authentication used in Imagga’s API from a given API_KEY and API_SECRET which you have to manually set in the first lines of the script.
There are three main functions in the script - upload_image, tag_image, extract_colors.
-
- upload_image(image_path) - uploads your file to our API using the content endpoint, the argument image_path is the path to the file in your local file system. The function returns the content id associated with the image.
- tag_image(image, content_id=False, verbose=False, language='en') - the function tags a given image using Imagga’s Tagging API. You can provide an image url or a content id (from upload_image) to the ‘image’ argument but you will also have to set content_id=True. By setting the verbose argument to True, the returned tags will also contain their origin (whether it is coming from machine learning recognition or from additional analysis). The last parameter is ‘language’ if you want your output tags to be translated in one of Imagga’s supported 50 (+1) languages. You can find the supported languages from here - http://docs.imagga.com/#auto-tagging
- extract_colors(image, content_id=False) - using this function you can extract colors from your image using our Color Extraction API. Just like the tag_image function, you can provide an image URL or a content id (by also setting content_id argument to True).
Script usage:
Note: You need to install the Python package requests in order to use the script. You can find installation notes here.
You have to manually set the API_KEY and API_SECRET variables found in the first lines of the script by replacing YOUR_API_KEY and YOUR_API_SECRET with your API key and secret.
Usage (in your terminal or CMD):
python tag_images.py <input_folder> <output_folder> --language=<language> --verbose=<verbose> --merged-output=<merged_output> --include-colors=<include_colors>
The script has two required - <input_folder>, <output_folder> and four optional arguments - <language>, <verbose>, <merged_output>, <include_colors>.
- <input_folder> - required, the input folder containing the images you would like to tag.
- <output_folder> - required, the output folder where the tagging JSON response will be saved.
- <language> - optional, default: en, the output tags will be translated in the given language (a list of supported languages can be found here: http://docs.imagga.com/#auto-tagging)
- <verbose> - optional, default: False, if True the output tags will contain an origin key (whether it is coming from machine learning recognition or from additional analysis)
- <include_colors> - optional, default: False, if True the output will also contain color extraction results for each image.
- <merged_output> - optional, default: False, if True the output will be merged in a JSON single file, otherwise - separate JSON files for each image.
Update Of Imagga Pricing Plans
We are excited to announce some changes to our API pricing policy. We’ve got lots of feedback and requests for more affordable ways to access our APIs.
Today, we are announcing Developer Plan for Imagga APIs, priced at $14/month that will allow the use of one of our APIs with up to 12 000 calls a month (3000/day, 2 requests/second). We believe this plan will bring on the table flexibility and the opportunity to apply our breakthrough technology on a more affordable price.
Hacker plan remains free but we are reducing the monthly calls to 2000 (200/day, 1 request per second) and will be available as before just for image tagging API.
We are eager to see you how gonna apply our technology in your projects! Send us feedback and any ideas you have regarding our technology offering in general or any tip you want to share.
Imagga in 2014
2014 was quite exciting and challenging for Imagga. One of the most important things that have happened is the significant improvement of out tagging technology. We’ve trained and learned to recognize new objects so the tags the tech returns are more relevant than ever. This wouldn't be possible without the committed efforts of our machine learning researchers and software engineers. We grew in numbers as well. We’ve also got a new website and better business offering - see our current pricing plans.
What would be the year without great hacker events. We’ve attended and partnered quite alot - Photo Hack Day NYC, Seedhack Lifelogging London, LDV Vision New York, Photo Hack Day Japan, Telerik Hackathon. It’s always nice to meet excited developers eager to get their hands dirty on our APIs.
The end of the year got us a nice surprise - awesome reward from Trento ITC Labs. Besides the cash, we are excited to be able to leverage on their research and business network and spend couple of weeks in Berlin and London.
What 2015 have in store?
We are getting ready for an exciting and quite intensive 2015.
- Several updates of the technology are pending - more concepts and objects to recognize
- great demo tools are in works that we believe will help us better explain the power behind Imagga image tagging technology
- personal photo categorization app
- discovering new verticals and awesome application of image recognition
If you haven’t tried our APIs, sign up, our hacker plan is free forever.
Can The Machine Beat Humans in Image Recognition
For far too long image understanding has been considered too complex for the machines to deal with. It takes years of training for the human brain to build links between the visible and connect it to concepts of shapes, colors and objects. Even though neural networks were invented couple of decades ago and were considered huge step into machine AI, what lacked was computing power. With the advance of GPU computing, new opportunities were discovered, algorithms were reinvented so machine and deep learning are back on the table.
The machines are powerful enough now to grasp the world almost as good as a 3 years old kid. A prerequisite for neural network to work well is a clear, representative data that will make the outcome results more precise and accurate. Huge efforts to collect and classify the images of the world were undertaken in the last couple of years. Are the machines ready for a battle then?
At Imagga we take that challenge seriously by building an intelligent image recognition technology that can teach the machine to understand basic daily life objects, comprehend concepts and eventually deal with complex pictures, where lots of background information needs to be taken into account in order to be interpreted properly. It’s challenging task but we love what we do.
With that stated, we are ready to set the stage for an epic battle, the battle of the century - machines vs humans. To some it might sound funny, unrealistic, pretentious, but it’s coming. At least now in a form of a cool game, done with love by Imagga and Algolia.
We’ve called it Human vs. Robot: Chash Of The Image Tags. You will be taking central role of judging who tags better - the human or the machine. You will be presented two sets of images for a given text tag and need to vote for the set that better represents the concept of the text tag. As every good judges you will need to be unbiased and make up your mind only on the facts, so you will not know which set was tagged by humans or respectively by machines. You will get five rounds to decide and pronounce a winner. Of course you can play as many times as you wish, and even invite your friends to try it out and have fun.
The game is made possible by the joined efforts of Algolia and Imagga. Algolia is building powerful search technology for exploration of large data sets. Algolia’s hosted search API delivers instant and relevant results as you type your search query. Imagga’s part is to provide the automated machine tagging of all the images you will be seeing in the search results.
It might be just a game, but the real idea is to demonstrate how powerful machine recognition is nowadays. It can really replace or at least greatly assist people in the process of tagging photos - it’s much faster, more cost effective, most of the time - more consistent and even more precise than human tagging. This empowers a lot of use-cases in stock photography, digital asset management, advertising, cloud storage and photo sharing that are otherwise not feasible or even not possible with human tagging.
Why don’t you play and judge for yourself Clash of the image tags!
Imagga Gets Big Players Award by HM King of Spain at South Summit 2015
Georgi Kadrev getting Tech for Big Players Award from HM Felipe VI, King of Spain[/caption]
Imagga was honored to get Tech for Big Players Award at South Summit 2015, that took place in Madrid, Spain. At the closing day HM Felipe VI, King of Spain himself awarded the finalists of the startup competition at the event and spent some time to talk with the winning companies. Out of the four startups being recognized at the event, Imagga is the only company that doesn't come from Spanish speaking country.
South Summit is the leading entrepreneurship event for the Spanish speaking world. Over 12,500 participants, 100 startups, 650 investors, 325 journalists и 275 speakers took place in the forum, held in the historic Las Ventas building - build in the beginning of 20 century and home of the famous bull fights. Special guest of the event was Steve Wozniak, co-founder of Apple together with Steve Jobs.
Imagga was one of the 100 carefully selected startups from 15 countries (3 companies in total from Bulgaria) and we competed in Tech for Big Players category (the other categories were; digital solutions for the mass market, healthcare and biotech, industrial revolution)
“Being able to stand and pitch on the very same arena famous for bullfighting actually raised the adrenalin and made the experience quite unique. It was great opportunity to meet fellow entrepreneurs, talk image recognition and AI, currently quite hot trends, and talk to investors and media”, shares Georgi Kadrev, co-founder and CEO of Imagga.
https://youtu.be/6UQirQBi2Jc?t=52m20s
Imagga's automated tagging is getting traction and we see great use cases and lots of business opportunities. South Summit opened up a new world of possibilities as we've never been that much focused on the Spanish speaking world, but we see it's big and interesting.
"I took some time to contemplate why I like this tree as a prize that much - it's not just because it was given by His Majesty the King of Spain and is the symbol of Madrid, but also as it is a very beautiful metaphor - it has been planted some time ago, and it took a while before it shows up above the ground. Now it's grown but it's still small and it still needs care until it grows and give some sweet fruits. I believe it's the same with our Imagga.", shares Georgi Kadrev
Being part of SouthSummit was fun and very rewarding. Thanks for inspiring us and at the same time acknowledging our efforts to democratize image recognition and make it useful for great variety of business use cases.
Multi Language Support - Imagga API
We are happy to announce we are adding 50 languages (still in beta) to Imagga Auto-tagging and Categorization APIs. The tags/categories that the powerful Imagga image recognition API returns now speak your language. All you need to do is to add the language parameter with corresponding language code you want the results to be displayed in. You can even add multiple languages (for example &language=de&language=fr).
Currently there are 50 languages supported with the following language codes: ar (Arabic), bg (Bulgarian), bs (Bosnian), ca (Catalan), cs (Czech), cy (Welsh), da (Danish), de (German), el (Greek), es (Spanish), et (Estonian), fa (Persian), fi (Finnish), fr (French), he (Hebrew), hi (Hindi), hr (Croatian), ht (Haitian Creole), hu (Hungarian), id (Indonesian), it (Italian), ja (Japanese), ko (Korean), lt (Lithuanian), lv (Latvian), ms (Malay), mt (Maltese), mww (Hmong Daw), nl (Dutch), no (Norwegian), otq (Querétaroo Otomi), pl (Polish), pt (Portuguese), ro (Romanian), ru (Russian), sk Slovak), sv (Swedish), sl (Slovenian), sr_cyrl (Serbian - Cyrillic), sr_latn (Serbian - Latin), th (Thai), tlh (Klingon), tlh_qaak (Klingon (pIqaD)), tr (Turkish), uk (Ukrainian), ur (Urdu), vi (Vietnamese), yua (Yucatec Maya), zh_chs (Chinese Simplified), zh_cht (Chinese Traditional)
Find out how to implement in Imagga API Docs.
You can see how auto-tagging and the language support works live on our demo page.
Not finding yours in the supported languages list? Talk to us.
Imagga Partners with Aylien
We are super excited to announce our partnership with AYLIEN - a natural language processing platform, that will make possible to add text analytics capabilities to our image recognition and analytics APIs. We believe this partnership will help users of both services better understand their multimedia content and do way more with it.
AYLIEN Text API is a package consisting of eight different Natural Language Processing, Information Retrieval and Machine Learning APIs that help developers extract meaning and insight from text documents. It can be applied in Ad-Targeting, Media Monitoring and Social Listening projects.
Imagga’s Image Recognition API utilizes machine learning, image recognition and deep learning algorithms to identify over 6,000 distinct objects and concepts and return relevant keywords that best describe what’s in the images.
Currently Imagga’s image analysis endpoint is being added to ALYIEN’s Natural Language Processing API, giving developers the ability to analyze text and images in one API.
Having access to two powerful technologies in a single API creates endless opportunities for businesses that need to deal with large volumes of user generated content. Users rarely input or share just text or images, so being able to analyze and understand both at once, gives amazing new opportunity for any business to distribute and monetize content.
Together with AYLIEN we’ve been testing how our technologies can compliment each other for some time and the results were very exciting. Text and images are different but complement well each other in many cases and applications.
You can try the new hybrid image and text analysis service here. There’s nice demo to play with before you are finally sold (you can see the results for some sample images, but also can upload your own)
Analyzing Text and Images: Webinar
Happy to announce an upcoming webinar together with our platform partners Ontotext. The webinar on Self-Service Semantic Suite (S4) will take place on Nov 12th (11am ET / 8am PT / 4pm GMT) featuring Ontotext CTO, Marin Dimitrov as well as a guest speaker Georgi Kadrev, CEO of Imagga.
S4 provides on demand access to text mining and linked open data technology in the cloud. Using the platform you can quickly and easily build smart data prototypes at a fraction of the cost of enterprise solutions.
Georgi (@imagga) of Imagga will be covering the newly available image analytics capabilities into Ontotext’s Self-Service Semantic Suite (S4) platform. Thanks to powerful Imagga auto tagging & categorization API, S4 users will get more insights not just from the text but also from images on the web pages they need to analyze.
Marin (@marin_dim) from Ontotext will be covering some new exciting features of Ontotext Semantic Suite as improvements of the RDF graph database-as-a-service, new Python SDK for S4 as well as Product roadmap and new features currently in development.
Don’t miss the opportunity to learn about the Self-Service Semantic Suite (S4) – register for this event now!